Lifeboat Foundation

Charles Darwin’s landmark opus “On the Origin of the Species” ends with a beautiful summary of his theory of evolution: “There is a grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.” In fact, scientists now know that most species that have ever existed are extinct.

This extinction of species has, on the whole, been roughly balanced by the origination of new ones over Earth’s history, with a few major temporary imbalances scientists call mass extinction events. Scientists have long believed that mass extinctions create productive periods of species evolution, or “radiations,” a model called “creative destruction.” A new study led by scientists affiliated with the Earth-Life Science Institute (ELSI) at Tokyo Institute of Technology used machine learning to examine the co-occurrence of fossil species and found that radiations and extinctions are rarely connected, and thus mass extinctions likely rarely cause radiations of a comparable scale.

Creative destruction is central to classic concepts of evolution. It seems clear that there are periods in which many species suddenly disappear, and many new species suddenly appear. However, radiations of a comparable scale to the mass extinctions, which this study, therefore, calls the mass radiations, have received far less analysis than extinction events. This study compared the impacts of both extinction and radiation across the period for which fossils are available, the so-called Phanerozoic Eon. The Phanerozoic (from the Greek meaning “apparent life”), represents the most recent ~ 550-million-year period of Earth’s total ~4.5 billion-year history, and is significant to palaeontologists: Before this period, most of the organisms that existed were microbes that didn’t easily form fossils, so the prior evolutionary record is hard to observe.

YesHealth Group and Nordic Harvest have completed the first phase of construction on Europe’s largest vertical farm. It stands 14 levels high in a 7,000 sq. metre facility at Copenhagen Markets, on the outskirts of Denmark’s capital.

Automatic bug fixer.

Software development bot that automatically repairs build failures on continuous integration. Join the bot revolution! :star2::robot::star2::revolving_hearts: — eclipse/repairnator.

Researchers at Hokkaido University and Amoeba Energy in Japan have, inspired by the efficient foraging behavior of a single-celled amoeba, developed an analog computer for finding a reliable and swift solution to the traveling salesman problem—a representative combinatorial optimization problem.

Many real-world application tasks such as planning and scheduling in logistics and automation are mathematically formulated as combinatorial optimization problems. Conventional digital computers, including supercomputers, are inadequate to solve these complex problems in practically permissible time as the number of candidate solutions they need to evaluate increases exponentially with the problem size—also known as combinatorial explosion. Thus new computers called Ising machines, including quantum annealers, have been actively developed in recent years. These machines, however, require complicated pre-processing to convert each task to the form they can handle and have a risk of presenting illegal solutions that do not meet some constraints and requests, resulting in major obstacles to the practical applications.

These obstacles can be avoided using the newly developed ‘electronic amoeba,’ an analog computer inspired by a single-celled amoeboid organism. The amoeba is known to maximize nutrient acquisition efficiently by deforming its body. It has shown to find an approximate solution to the traveling salesman problem (TSP), i.e., given a map of a certain number of cities, the problem is to find the shortest route for visiting each city exactly once and returning to the starting city. This finding inspired Professor Seiya Kasai at Hokkaido University to mimic the dynamics of the amoeba electronically using an analog circuit, as described in the journal Scientific Reports. “The amoeba core searches for a solution under the electronic environment where resistance values at intersections of crossbars represent constraints and requests of the TSP,” says Kasai. Using the crossbars, the city layout can be easily altered by updating the resistance values without complicated pre-processing.

Simone Pika and colleagues tested the cognitive skills of eight hand-raised ravens at four, eight, 12 and 16 months of age using a series of tests. The skills the authors investigated included spatial memory, object permanence—understanding that an object still exists when it is out of sight—understanding relative numbers and addition, and the ability to communicate with and learn from a human experimenter.

The authors found that the cognitive performance of ravens was similar from four to 16 months of age, suggesting that the speed at which the ravens’ cognitive skills develop is relatively rapid and near-to-complete by four months of age. At this age ravens become more and more independent from their parents and start to discover their ecological and social environments. Although task performance varied between individuals, ravens generally performed best in tasks testing addition and understanding of relative numbers and worst in tasks testing spatial memory.

Scientists find routes using arches of chaos that can lead to much faster space travel.

Circa 2019

In a feat sure to fire up ethical and philosophical debate, a new system has restored circulation and oxygen flow to a dead mammal brain.

Drones with flamethrowers! 😃 They seem to be using it to burn hornets nests and debris.

This flamethrower drone can shoot a 23-foot-long stream of fire.